Multiscale Modeling of Esophageal Adenocarcinoma

Abstract

Over the past three to four decades, esophageal adenocarcinoma (EAC) incidence has increased dramatically in the Western world due to causes that are not well understood. Current screening strategies for early detection aim to identify individuals with Barrett's esophagus (BE), an intestinal metaplasia that develops in the lower esophagus as an important first step in the progression to EAC. However, current approaches for prevention of EAC by screening and surveillance programs have achieved minimal success in reducing mortality and paradoxically yield underdiagnosis and overdiagnosis. In order to better understand these issues, we consider the influences of critical processes at multiple spatial scales in an effort to bridge molecular, cellular and tissular knowledge to population-level data related to BE and the progression of BE to EAC. Specifically, the mathematical framework presented here cohesively models biological mechanisms that include epigenetic drift, cellular dynamics, clonal growth, crypt structured organization in BE, spatial propagation of premalignant and malignant lesions, surveillance through biopsy and imaging, and clinical interventions. With the multiscale modeling approach, we can better understand the role and impact that different levels of data have on clinical outcomes. Our modeling aim is to ultimately improve the efficacy of screening and surveillance to reduce EAC mortality.